XGR (version 1.1.4)

xRWenricher: Function to perform connectivity enrichment analysis on the input graph

Description

xRWenricher is supposed to perform connectivity enrichment analysis on the input graph given a list of nodes of interest. The test statistic is the average affinity between the given nodes. The pairwise affinity between two nodes is estimated via short random walks. The null distribution of the test statistic is generated by permuting node labels on the graph (fixed) in a centrality-preserving manner. In brief, all nodes are equally binned by centrality (defined as the mean affinity to all other nodes), and a permuted instance is generated by randomly sampling (a same number of) nodes from the same bin. The connectivity ratio is the observed divided by the expected (the median across the permutations), together with the empirical p-value.

Usage

xRWenricher(data, g, Amatrix = NULL, num.permutation = 2000, nbin = 10,
steps = 4, chance = 2, seed = 825, verbose = TRUE)

Arguments

data

a vector containing node names

g

an object of class "igraph" or "graphNEL". It will be a weighted graph if having an edge attribute 'weight'. The edge directions are ignored for directed graphs

Amatrix

an affinity matrix pre-computed from the input graph. It is symmetric

num.permutation

the number of permutations generating the null distribution

nbin

the number of bins dividing all nodes into the equal number of nodes

steps

an integer specifying the number of steps that random walk performs. By default, it is 4

chance

an integer specifying the chance of remaining at the same vertex. By default, it is 2, the higher the higher chance

seed

an integer specifying the seed

verbose

logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display

Value

a data frame with 9 columns:

• Obs: the observed affinity between the given nodes

• Exp: the expected affinity between the permuted nodes

• std: the standard deviation of the expected

• fc: fold change

• zscore: z-score

• pvalue: p-value

• or: a vector containing odds ratio

• CIl: a vector containing lower bound confidence interval for the odds ratio

• CIu: a vector containing upper bound confidence interval for the odds ratio

See Also

xRWenricher

Examples

# NOT RUN {
# 1) generate a random graph according to the ER model
set.seed(825)
g <- erdos.renyi.game(10, 3/10)
V(g)$name <- paste0('n',1:vcount(g))

# }
# NOT RUN {
# 2) perform connectivity enrichment analysis
data <- V(g)$name[1:3]
res <- xRWenricher(data, g, nbin=2)
# }